Covert Naming Task EEG Data Denoised for 25 Participants

Gilmore, Adrian provides estimated EEG data from 25 participants in a covert naming experiment. The data has undergone a complete denoising procedure including gradient artifact and ECG removal, as well as regression of signals near the eyes and upper jaw. ICA components summing to 90% of the variance around response times have also been removed.

Use Cases

• Analyze neural correlates of covert language production based on denoised EEG signals.
• Study the effect of artifact removal on EEG data quality based on the described denoising procedure.
• Investigate brain activity patterns around response times based on the ICA component removal criteria.
• Benchmark new EEG denoising algorithms against the described multi-step procedure.

Strengths

• Data from 25 participants provides a sample for analysis.
• A multi-step denoising procedure including gradient artifact, ECG, and ocular/muscular signal removal was applied.
• ICA components accounting for 90% of variance around response times were removed.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are unknown, which may limit suitability assessment.
• The description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Gilmore, Adrian; Data and code from: Repetition-related reductions in neural activity support improved behavior through increases in oscillatory power

Collection Method

Collected from participants in a covert naming experiment, with subsequent denoising.

Freshness

Last updated 2026-04-25 00:09:30